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Short Bio
I am currently at Google Brain/AI in Pittsburgh, working on hyperparameter optimization, Bayesian methods, and theoretical deep learning.
I am grateful to have graduated with a PhD in Applied Mathematics and Computer Science at UC Berkeley, where I was fortunate to be advised under Prof. Satish Rao and Prof. Nikhil Srivastava. My interests are in the intersection of optimization, theoretical computer science and machine learning. Previously, I graduated in the Great Class of 2014 from Princeton University.
Papers
Random Hypervolume Scalarizations for Provable Multi-Objective Black Box Optimization
with Daniel Golovin.
ICML 2020.Gradientless Descent: High-Dimensional Zeroth-Order Optimization [slides]
with Daniel Golovin, John Karro, Greg Kochanski, Chansoo Lee, Xingyou Song.
ICLR 2020 (Spotlight).Span Recovery for Deep Neural Networks with Applications to Input Obfuscation
with Rajesh Jayaram, David Woodruff.
ICLR 2020.Regularized Weighted Low Rank Approximation
with Frank Ban, David Woodruff.
NEURIPS 2019.Solving Empirical Risk Minimization in the Current Matrix Multiplication Time
with Zhao Song, Yin Tat Lee.
COLT 2019.Optimal Sequence Length Requirements for Phylogenetic Tree Reconstruction with Indels
with Arun Ganesh.
STOC 2019.Using INC Within Divide-and-Conquer Phylogeny Estimation
with Thien Le, Aaron Sy, Erin Molloy, Satish Rao, Tandy Warnow.
AlCoB 2019.New Absolute Fast Converging Phylogeny Estimation Methods with Improved Scalability and Accuracy
with Satish Rao, Tandy Warnow.
WABI 2018.Convergence Results for Neural Networks via Electrodynamics
with Rina Panigrahy, Sushant Sachdeva.
ITCS 2018.Forbidden Directed Minors and Kelly-width
with Shiva Kintali.
Theoretical Computer Science 2017, Vol. 662.
Contact
- Email: qiuyizhang (at) gmail (dot) com
- Office: Google Pittsburgh in Bakery Square